For the coronary CT, modern CT scanners whose detector rows are ranging from 64 to 80 need to select a temporary reconstruction window in the acquired data since the entire heart organ cannot be scanned at the same location. In the absence of state-of-art multislice CT such as having 320 rows of detectors to scan the entire heart, it is thus necessary to generate a cardiac phase map in order to select a certain portion of projection data that corresponds to the optimal cardiac phases before reconstructing cardiac images that are substantially free from artifacts due to motion of the heart.
It has been well known that the optimal cardiac phases are generally periodic and may be near an end of the systolic phase and near a mid-point of the diastolic phase in the cardiac cycle. In these optimal cardiac phases, the heart experiences a relatively small amount of motion. To determine the optimal cardiac phases, one exemplary method is to use electrocardiogram (ECG) relative to R-to-R interval. Although ECG has been used both to trigger the date acquisition and to select the optimal reconstruction window, ECG represents the electrical signal of the heart and generally does not necessarily represent the mechanical state of the heart or the cardiac motion.
Unfortunately, it remains challenging to consistently find the optimal cardiac phases for reconstruction among patients since they depend upon patient-to-patient variability as well as inter-cycle variability of the same patient. That is, since the exact optimal cardiac phases within the cardiac cycle vary from one patient to another, there is no single quantitative method to determine the exact optimal cardiac phases among the patients. Furthermore, the optimal cardiac phases may vary within the same patient due to certain factors including an irregular heart rate such as arrhythmia. For the above reasons, a patient-specific cardiac phase map is generated in order to select a certain portion of projection data that corresponds to the optimal cardiac phases before reconstructing substantially artifact-free cardiac images for a particular patient.
In most prior art techniques, the heart is assumed to be moving in a uniform manner over various parts of the organ. Although the heart movement is complex and not necessarily uniform over the organ, the assumption simplifies the complex nature of the heart movement so that when an optimal phase is selected, the heart experiences the least amount of movement and a corresponding view is at least substantially free from movement regardless of a location in the heart.
In general, in comparison to a relatively short scan time using high-speed multislice helical CT, the post-scan time needed to select an optimal cardiac phase accounts for a large percentage of the coronary CT angiography examination time. Prior art selection techniques have attempted to reduce the above phase selection time while maintaining the accuracy in selecting the optimal cardiac phase for the image reconstruction.
Many prior art selection techniques rely on the image domain. That is, the optimal cardiac phase is determined based upon image data that are reconstructed from the original projection data or raw data. For example, low-resolution images have been reconstructed through the cardiac cycle after the coronary helical scan using a contrast-enhancement agent. Based upon the low-resolution images, periods of the least differences were selected between the neighboring phases indicating minimal cardiac motion. Although the low-resolution image data such as 64×64×64 voxels are reconstructed, these prior art techniques were not efficient due to the above time-consuming calculation during the reconstruction. Furthermore, these prior art techniques also suffered large cone angle problems and could not give accurate results at high-pitch helical scan.
Another prior art technique reduced the phase selection time based upon an automatic cardiac phase selection algorithm. In stead of the image domain, Ota et al. calculated the absolute sum of the differences between two raw helical scan data sets for subsequent cardiac phases and generated a velocity curve representing the magnitude of cardiac motion velocity for the entire heart volume. FIG. 1 illustrates partial row data at a target slice position along the axial plane including all four cardiac chambers (right atrium, right ventricle, left atrium, and left ventricle) indicated by a horizontal line. Since multi-slice computer tomography (MSCT) systems have multiple detector rows, the raw data corresponding to the amount of time required as the detector rows passed through the target slice position were generated by performing helical interpolation between detector rows. The helically interpolated raw data were consecutive dynamic scan raw data in the time axis direction at the same couch position while the raw data containing different timing information (cardiac phases) were obtained by extracting the raw data corresponding to a half scan from the sequential raw data according to the ECG-gating signals (timing shift technique). FIG. 1 also illustrates the partial raw data at cardiac phases of 0%, 10%, and 20%. These partial data correspond to the same couch position as the target slice position, but have different timing information. By the same token, the cardiac motion velocity of the heart is extracted at intervals of 2% by obtaining the sum of absolute values of the differences (SAD) at intervals of 4% for the raw data obtained at intervals of 2%.
The above prior art technique has improved one aspect in efficiency but has left other aspects unimproved. For example, the SAD was calculated from two chunks of views corresponding to adjacent phases, and the velocity curve was derived from the SAD. Furthermore, since the sinogram data is generated from interpolated rows in the measured data (i.e., it did not consider a cone angle), as the cone angle and the helical pitch increased, the sinogram became less accurate in comparison to the real measured data. Thus, SAD according to the Ota et al. prior art technique failed to accurately determine the optimal phase.
The previously discussed prior art techniques commonly utilized low pitch settings, raging from 0.1 to 03 for helical scan. These low pitch settings also translated to a higher x-ray dose to patients since regions exposed to the x-ray radiation are highly overlapped. From patient safety, a low dosage level is desired particularly for a repeated necessity for CT imaging.
In view of the above prior art problems, another technique is based upon step-and-shoot (SAS) cardiac imaging for determining the optimal cardiac phase for gating and reconstruction. That is, in one exemplary SAS data acquisition, the patient table remains stationary at one location while the x-ray tube and gantry rotate about the patient so that 64 slices are simultaneously collected. When irregular heart rate such as arrhythmia is encountered during the data acquisition, the data acquisition continues at the same location for the next normal heart cycle. After the data acquisition is completed for the one location, the table is stepped to the next location for a subsequent scan. For each step, the table traveled over 40 mm, which is roughly equal to the x-ray beam width, and little overlap is encountered in the exposure. Because the use of circular scan SAS data is proposed instead of helical scan data, the prior art technique reduced x-ray exposure to the patient and overcame some of the longitudinal truncation problems associated with helical scan data.
The above prior art technique utilized the conjugate samples to determine an amount of the heart motion. The conjugate samples were also each a pair of complementary rays as seen in the raw projection data. For a set of fan-beam data set, a pair of conjugate samples was defined by (γ, β) and (−γ, β+π+2γ), where γ and βwere respectively the fan angle and the projection angle. If γm were used to denote the maximum fan angle, the minimum cone beam data collection would be carried out in the view range of π+2γm. The entire dataset was searched through to identify all the conjugate samples, and the total absolute difference was calculated as below:ξ(β0)=∫−γmγm∫β0β0+Π|p(γ,β)−p(−γ,β+Π+2γ)|dγdβ
where Π<<π+2γm was the angular range of the consistency condition evaluation. Since the quantity ξ(β0) indicated the degree of inconsistency amongst all evaluated conjugate samples and the conjugate samples represented line integrals along the same path, the consistency, ξ, is a measure of the heart motion.
The above prior art utilized the conjugate samples in the SAS data to reduce the patient dosage exposure and to improve in determining the cardiac motion. On the other hand, the SAS technique has its disadvantages. Although the cardiac cycle is continuous, the SAS technique cannot capture the continuous cardiac cycle since the data acquisition is discontinuous over the various locations over the heart. After the data acquisition at a first location, no data is acquired while the patient table is stepped to a subsequent location. That is, the SAS data are not collected within the same cardiac cycle or over the continuous cardiac cycles. Furthermore, the projection data using the SAS technique may have overlapping portions or missing portions due to alignment of the projection angle. Lastly, if a contrast agent is used, these discrete delays between actual data acquisitions may not reflect the gradually declining effectiveness of the contrast agent.
In view of the above disadvantages, the helical data appear to be advantageous for the determination of the optimal cardiac phases in reconstruction. While the use of the conjugate samples also appears efficient in determining the optimal cardiac phases, the conjugate samples is limited to the SAS data and cannot be applicable to the helical data.
In summary, among the prior art attempts of generating cardiac phase maps, certain disadvantages remain to be desired. These disadvantages include efficiency, artifacts and helical scanning limitations. To generally improve these disadvantages in the cardiac CT application, the cardiac phase map must be efficiently generated, and a desirable phase should be selected within a reasonable time. At the same time, the optimal phase selection should be accurately determined so as to minimize artifacts in reconstructed images. Lastly, the projection data should be helically acquired in order to continuously reflect motion over the cardiac cycle.